Identifying relations between imaging phenotypes and molecular subtypes of breast cancer: Model discovery and external validation

Abstract

Purpose: To determine whether dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) characteristics of the breast tumor and background parenchyma can distinguish molecular subtypes (ie, luminal A/B or basal) of breast cancer.

Materials and methods: In all, 84 patients from one institution and 126 patients from The Cancer Genome Atlas (TCGA) were used for discovery and external validation, respectively. Thirty-five quantitative image features were extracted from DCE-MRI (1.5 or 3T) including morphology, texture, and volumetric features, which capture both tumor and background parenchymal enhancement (BPE) characteristics. Multiple testing was corrected using the Benjamini-Hochberg method to control the false-discovery rate (FDR). Sparse logistic regression models were built using the discovery cohort to distinguish each of the three studied molecular subtypes versus the rest, and the models were evaluated in the validation cohort.

Results: On univariate analysis in discovery and validation cohorts, two features characterizing tumor and two characterizing BPE were statistically significant in separating luminal A versus nonluminal A cancers; two features characterizing tumor were statistically significant for separating luminal B; one feature characterizing tumor and one characterizing BPE reached statistical significance for distinguishing basal (Wilcoxon P < 0.05, FDR < 0.25). In discovery and validation cohorts, multivariate logistic regression models achieved an area under the receiver operator characteristic curve (AUC) of 0.71 and 0.73 for luminal A cancer, 0.67 and 0.69 for luminal B cancer, and 0.66 and 0.79 for basal cancer, respectively.

Conclusion: DCE-MRI characteristics of breast cancer and BPE may potentially be used to distinguish among molecular subtypes of breast cancer.

Level of evidence: 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017;46:1017-1027.

Keywords: breast cancer; classification; dynamic contrast enhanced MRI; imaging genomics; molecular subtype.

Figure 1

Flowchart of the complete design of the proposed study.

Figure 2

Schematic illustration of quantitative DCE MR imaging features extraction procedure. DCE-MRI: dynamic contrast-enhanced magnetic resonance image.

Figure 3

Clustering analysis of the quantitative imaging features from both discovery and validation cohorts. In the heat map, all 35 features (presented in different rows and color-coded by the region and type) from all 210 patients (presented in each column) were correlated with their molecular subtype (color-coded on the top bar). All features were standardized to have a zero mean and unit standard deviation. BPE: background parenchymal enhancement.

Figure 4

Imaging features significantly associated with molecular subtypes (after correction for multiple testing) in both discovery and validation cohorts, a–d) 4 features for distinguishing luminal A versus non-luminal A, e–f) 2 features for distinguishing luminal B versus non-luminal B, and g–h) 2 features for distinguishing basal-like versus non-basal-like. Wilcoxon rank sum test was implemented to investigate pairwise difference. Also, the false discovery rate (FDR) adjusted for multiple testing was reported.

Figure 5

ROC curves of the built imaging signature and the baseline model with three clinical factors (age, menopausal status, and histologic type) trained in the discovery cohort to distinguish a) luminal A, b) luminal B, and c) basal like cancer type. The corresponding AUCs and 95% confidence intervals were reported.

Figure 6

Validation of previously built models from discovery cohort in the independent cohort to distinguish a) luminal A, b) luminal B, and c) basal like cancer type. The corresponding AUCs and 95% confidence intervals were reported.